Dimensional ModelingThe Favorite Data Modeling Methodology

Data is enabled to be modeled in a range of different ways, and dimensional modeling is one of them. The best characteristic of dimensional modeling is its ability to allow data to be stored in a way that is optimized for information retrieval once the same is stored in a database. At the time of building a data warehouse for business intelligence, SQL server dimensional modeling enables the following:

  • Stored data to be easily deciphered when viewed as a reporting schema whereupon relationships between tables are distinctly labeled
  • Quicker querying throughout your enterprise systems; lesser joins are required to exhibit query results due to the denormalized structure
  • Mobility to modify or extend data models to redesign them as business requirements change, clearing the way for an agile data warehousing approach
Dimensional Modeling and Relational Modeling Are Different

Based on the business needs, your data model can be either relational or dimensional. A relational model is a design methodology that strives to remove data redundancy. Data is bifurcated into multiple discrete entities, each of which becomes a table in the relational database. The tables are usually normalized to the 3rd normal form. In OLTP applications, the same methodology is used.

When it comes to warehousing requirements, data is denormalized to escalate performance. In a dimensional model, the data is bifurcated into ‘dimensions’ and ‘facts’ whose design objectives are user clarity, query performance, and resilience to change.

SQL server dimensional modeling is even now the most commonly used data modeling methodology for designing enterprise data warehouses owing to the rewards it yields. Those are the following:

Quick Retrieval of Data

Dimensional modeling allows for the merger of the tables in the model itself, which makes it easy for users to retrieve data quickly from diverse data sources by running join queries. Optimizing of the denormalized schema of a dimensional model data warehouse is done to run ad hoc queries. Because of this, it exceedingly complements the business intelligence (BI) objectives of an organization.

Greater Clarity of Business Processes

The fundamentals of dimensional modeling rely on fact and dimension tables. This classification of data into facts and dimensions, and the entity-relationship structure of a dimensional model, exhibit complex business processes in a clear and precise manner to analysts.

Bottom Line

These are some outstanding features and functions of dimensional modeling that has made it a favorite data modeling methodology.